DC 1 - Spatial SCENITH: Advancing In Vivo Immunometabolism Research through Spatially Resolved Functional and Multi-Omics Analysis

• Objectives:

Aim of the project: To develop and implement original tools to dissect the molecular mechanisms on how glycolytic stress rewires the glycolysis–lipid metabolic axis across tissues and immune cell types, and to build a next-generation toolbox enabling functional, spatial and multi-omic mapping of immunometabolism

1. develop Spatial-SCENITH and Epic-SCENITH for ex vivo microscopy and high-content imaging, and transcriptomics to measure glycolytic, mitochondrial, and lipid-driven metabolic states with spatial resolution;
2. establish standardized immunometabolic cytometry panels for profiling oxidative stress, lipid metabolism, mitochondrial fitness, and nutrient-sensing pathways across tissues and disease models;
3. collaborate with DC5 to develop machine-learning models that integrate spatial, functional, and lipidomic datasets into unified metabolic state maps.

• Brief project description:

Immune cells must constantly adapt their metabolism to fluctuating nutrient availability and microenvironmental stress. A central decision point in this adaptation is the glycolysis-to-lipid metabolic axis, which determines how cells balance rapid ATP generation, biosynthetic needs, mitochondrial fitness, and effector functions.

Under glycolytic stress, immune cells engage emergency metabolic programs, reshaping mitochondrial activity, lipid usage, and signalling pathways. Despite the fundamental importance of this switch, it remains poorly understood how these stress-driven metabolic states emerge in tissues, how they vary across cell types, and how they shape immune function during inflammation, infection, or cancer.

Recent technological advances now make it possible to interrogate these processes with unprecedented resolution. Building on methodologies developed within the network—SCENITH, Epic-SCENITH, Spatial-SCENITH, high-content imaging, high-resolution lipidomics, and multi-omics machine learning—this project will generate the first integrated map of glycolytic stress-induced metabolic states at single-cell and spatial resolution.

• Planned secondment(s):

Dr. Bart Everts (Leiden University Medical Center, Netherlands)
• Generation of spatial transcriptomics and Spatial-SCENITH, and development of multiparametric panels for metabolic analysis.

Dr. David Sancho (CNIC, Madrid, Spain)
• Applying Spatial-SCENITH to explore metabolic alterations in vivo and ex vivo.

Dr. Ingrida Olendraite (Vugene, Vilnius, Lithuania)
• Integrating multi-omic analyses.